The effects of neural gain on attention and learning

Transcription

1 The effects of neurl gin on ttention nd lerning Ern Eldr 1, Jonthn D Cohen 1,2 & Yel Niv 1,2 213 Nture Americ, Inc. All rights reserved. Attention is commonly thought to be mnifest through locl vritions in neurl gin. However, wht would be the effects of brin-wide chnges in gin? We hypothesized tht globl fluctutions in gin modulte the bredth of ttention nd the degree to which processing is focused on spects of the environment to which one is predisposed to ttend. We found tht mesures of pupil dimeter, which re thought to trck levels of locus coeruleus norepinephrine ctivity nd neurl gin, were correlted with the degree to which lerning ws focused on stimulus dimensions tht individul humn prticipnts were more predisposed to process. In support of our interprettion of this effect in terms of globl chnges in gin, we found tht the mesured pupillry nd behviorl vribles were strongly correlted with globl chnges in the strength nd clustering of functionl connectivity, s brin-wide fluctutions of gin would predict. When presented with set of stimuli, some people my ttend to, nd therefore lern most bout, concrete visul detils, wheres others my ttend to bstrct semntic concepts ssocited with those stimuli. Evidence suggests tht such vritions in ttention nd lerning my reflect stble individul predispositions 1 3. We hypothesize tht the expression of these predispositions is modulted by globl vritions in neurl gin. Specificlly, we propose tht high gin focuses ttention nd lerning on dimensions of the environment to which one is predisposed to ttend, wheres low gin brodens ttention, thereby wekening the constrint of prior dispositions on ttention nd lerning. By wht mechnism cn gin exert these effects on ttention? Neurl gin cn be thought of s n mplifier of neurl communiction: when gin is incresed, excited neurons become even more ctive nd inhibited neurons become even less ctive 4 (Fig. 1). Existing evidence suggests tht the locus coeruleus norepinephrine (LC-NE) system serves to modulte neurl gin throughout the brin 5 1. We used simple neurl network model in which different neurl representtions compete through mutul inhibition to demonstrte tht pplying high globl level of gin to ll network units cn mke strong neurl representtions even more dominnt while further wekening weker competing representtions. Accordingly, we hypothesized tht high gin results in processing tht is more nrrowly focused on the most strongly represented fetures of perceived informtion. To test our hypotheses, we used tsk tht quntifies the degree of lerning bout perceptul versus semntic fetures of stimuli (Fig. 1b), together with stndrd trit questionnire tht ssesses predispositions to ttend to nd lern bout perceptul versus semntic dimensions of stimuli 3. In generl, we expected prticipnts to exhibit better lerning for the type of fetures (perceptul or semntic) to which they re predisposed. Notbly, we hypothesized tht this reltionship would be modulted by neurl gin. Although it is impossible to directly mesure gin in humn prticipnts, pupil dimeter, which is esily mesured noninvsively, hs been suggested to trck levels of neurl gin 5. Converging evidence suggests tht bseline pupil dimeter is correlted with tonic levels of LC-NE ctivity in rts, cts nd monkeys 5,11, s well s with humn behviors tht re predicted to be ssocited with tonic LC-NE ctivity in vriety of experimentl tsks nd mnipultions 6,7,12,13. Although bseline pupil dimeter between individuls cn be used to monitor chnges in gin in individuls, phsic pupil diltions normlized to bseline dimeter re better suited for between-subject comprisons, s they re better dissocited from fctors tht cn confound betweensubject bseline mesures. Becuse phsic responses re inversely relted to bseline pupil dimeter nd tonic LC-NE ctivity 5, pupil diltion responses provide n inverse mesure of tonic gin. To test neurl predictions of our gin-modultion hypothesis concerning the reltionship between pupil dimeter nd the mtch between individul predispositions nd lerning performnce, we used functionl mgnetic resonnce imging (fmri). First, incresed gin implies tht neurl signls re enhnced, which in turn predicts tht interctions between connected prts of the network should increse. Indeed, gin modultion hs been proposed s mechnism for flexible control of network functionl connectivity This suggests tht globl fluctutions in neurl gin should be ssocited with globl fluctutions in the strength of functionl connectivity nd tht these fluctutions in functionl connectivity should correlte with chnges in pupil dimeter. To test these predictions, we mesured the degree to which fluctutions in functionl connectivity within brin res were correlted between res, s well s with chnges in pupil dimeter. Second, our neurl network modeling of the effect of gin on lerning suggested tht the link between gin nd focused lerning should be medited by more tightly clustered pttern of neurl interctions through which processing is selectively focused on prticulr input strems. In contrst, when gin in the model ws low, widely distributed interctions medited the concurrent processing of multiple stimulus fetures. Accordingly, we predicted tht within-prticipnt 1 Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA. 2 Deprtment of Psychology, Princeton University, Princeton, New Jersey, USA. Correspondence should be ddressed to E.E. (eeldr@princeton.edu). Received 3 Mrch; ccepted 29 April; published online 16 June 213; doi:1.138/nn.3428 nture NEUROSCIENCE dvnce online publiction

2 bseline pupil dimeter would correlte throughout the experiment with the degree to which functionl connectivity ws clustered, s mesured using grph-theoretic nlysis 17, nd tht the degree of clustering would, in turn, correlte with bis in lerning performnce towrd the type of fetures tht individul prticipnts were predisposed to process. to represent. The simultions lso reveled tht gin ffects communiction ptterns in the network: multiple input strems interct with lower gin (Fig. 1c), wheres wek input strems hve less of n effect on other prts of the network with higher gin, with the result tht network connectivity is more tightly clustered nd seprte subnetworks re formed (Fig. 1d). 213 Nture Americ, Inc. All rights reserved. RESULTS A model of neurl gin nd predispositions in lerning First, to formlize our hypothesis bout the effect of gin on ttention nd lerning, we constructed simple neurl network model of the tsk tht lerned stimulus-rewrd reltionship from exmples (Fig. 1c,d). The input to the network consisted of two seprte strems of informtion, ech representing one dimension (for exmple, visul or semntic). One feture in ech dimension ws ssocited with monetry rewrd nd the other ws not. We simulted predisposition to ttend to one dimension more thn to the other by mking connection weights in one strem stronger thn those in the other strem. We then exmined the degree to which the network lerned to ssocite the rewrd-predicting feture in ech strem with rewrd output s function of both the predisposition of the network nd the level of gin. With low gin, inputs from both the strong nd wek strems propgted to the subsequent lyers (Fig. 1c) nd the reltionship with rewrd ws lerned for both types of fetures (tht is, predisposition did not significntly bis lerning; Fig. 1e). In contrst, when gin ws high, inputs in the strong strem dominted representtions in the middle lyer (Fig. 1d) nd lerning of the input-rewrd reltionship tended to proceed only on strongly represented fetures (tht is, lerning ws bised towrd fetures tht the network ws predisposed to represent; Fig. 1e). Thus, the simultions indicte tht incresed gin cn focus lerning on those fetures tht the network is predisposed b Tril 1 Pupil responses nd dherence to predispositions To test for the predicted reltionship between pupil responses (s n index of neurl gin) nd the influence of ttentionl predispositions on lerning, we sked prticipnts to choose between pirs of multidimensionl imges (comprised of visul nd semntic fetures) nd rewrded them ccording to their choices. Unbeknownst to the prticipnts, one visul feture nd one semntic feture predicted monetry rewrd in ech stimulus set (Fig. 1b). For exmple, in one stimulus set, office-relted imges, but not food-relted imges (semntic fetures), yielded rewrd nd, similrly, gryscle imges, but not color imges (visul fetures), yielded rewrd (rewrds were dditive so tht gryscle office-relted imge yielded twice the rewrd). Throughout 18 gmes, ech with different semntic nd visul dimensions nd unique stimuli, we mesured prticipnts visul nd semntic performnce seprtely using trils in which stimuli differed on either the visul or the semntic fetures, but not both. In ddition, we ssessed ech prticipnt s predisposition to process either the visul or semntic fetures using the Index of Lerning Styles (ILS) questionnire 3. The ILS questionnire contrsts sensing lerning style tht indictes predisposition to process nd lern bout sense-relted dt, such s visul fetures, with n intuitive lerning style tht indictes predisposition to lern bout bstrct concepts, such s semntic ctegories. We found tht more intuitive (nd less sensing) lerning style ws correlted with better performnce on the semntic trils thn on the c Low gin Rewrd d High gin Rewrd Output Low gin High gin Tril 2 Inhibition Net input Excittion Fetures of type 1 Figure 1 The effect of gin on ttention nd lerning. () Input-output function of neuron with low nd high gin. High gin mplifies the effect of input on output, cusing stronger excittion nd inhibition. (b) Experimentl design of the visul nd semntic lerning tsk. In ech tril, prticipnts were presented with choice between two imges (objects or words). Prticipnts were rewrded ccording to their choices, with counterfctul rewrds lso being displyed. In this prticulr gme, to mximize rewrd, prticipnts hd to lern by tril nd error tht office-relted imges provided higher rewrd thn food-relted imges (semntic fetures), nd tht gryscle imges provided higher rewrd thn color imges (visul fetures). Ech tril involved two new stimuli. (c,d) Simple rewrd-lerning neurl network. Arrows denote excittory connections, round edges denote inhibitory connections. Drker fill color indictes more ctivity nd thicker lines indicte stronger weights in exmples with low (c) nd high (d) gin. With high gin, ctivity of strongly represented fetures (type 1) block ctivity of wekly represented fetures (type 2) t the middle lyer (circled), so the mpping between type 2 fetures nd rewrd cnnot be lerned. This condition effectively seprtes the second input strem from the rest of the network. (e) Simulted lerning of mpping between the rewrd-predicting fetures nd rewrd. The reltive Fetures of type 2 Fetures of type 1 Simulted lerning Type 1 feture Type 2 feture Input weights rtio strength of lerning for the two fetures is shown s function of the rtio between the input weights (vried between 1/2 nd 2/1), for different levels of gin. The higher the gin, the more lerning performnce depends on the reltive weight of ech input strem. e Lerning performnce Type 2 feture Type 1 feture Fetures of type 2 Neurl gin 2.1 dvnce online publiction nture NEUROSCIENCE

3 213 Nture Americ, Inc. All rights reserved. Figure 2 Reltionship between lerning performnce nd ILS scores. () Difference in lerning bout semntic nd visul fetures in the behviorl experiment s function of sensing-intuitive score on ILS questionnire. Negtive vlues indicte better visul performnce (y xis) nd sensing lerning style (x xis), wheres positive vlues indicte better semntic performnce nd n intuitive lerning style (n = 35 prticipnts). (b) Correltion between ILS sensing-intuitive score nd visul-semntic performnce difference on the tsk (s shown in ) s function of men pupil diltion response. To exmine the degree to which tsk performnce mtched ILS score in prticipnts with different levels of pupil response, we divided prticipnts into five bins ccording to men pupil diltion. Ech dt point represents group of seven prticipnts. To illustrte, dt points from the individul members of the group with lowest men pupil response pper in blck in. (c) Pupil dimeter normlized by its vlue t tril onset (time ), verged within prticipnts cross trils nd then cross prticipnts (gry indictes s.e.m. cross prticipnts; n = 28 prticipnts). Pupil diltion response ws computed s the difference between the pek pupil dimeter during the 4 s tht followed tril onset nd the pre-tril bseline dimeter, normlized by the pre-experiment resting dimeter. As expected, bseline pupil dimeter nd pupil response were nticorrelted in ll prticipnts (men r =.77, rnge =.89 to.54, t 27 = 28.9, P < 1 21 ). Although bseline dimeter is thought to be more direct indictor of tonic LC-NE function, the normlized pupil diltion response cn serve s n inverse index tht is comprble between individuls. (d,e) Repliction of behviorl results in the fmri experiment with different group of prticipnts (n = 3 prticipnts). Ech dt point in e represents group of six prticipnts. visul trils (r =.28, P =.5, one tiled; Fig. 2 nd Supplementry Fig. 1), consistent with predisposition to ttend to nd lern bout semntic versus visul fetures of the stimuli. Notbly, the degree to which tsk performnce mtched individul predisposition ws strongly nticorrelted with men pupil diltion response cross individuls (r =.96, P <.1; Fig. 2b,c). Given the inverse reltionship between pupil response nd gin discussed bove, our finding suggests tht the ssocition between tsk performnce nd individul predisposition ws itself ssocited with high gin. These behviorl results were fully replicted in second experiment in which different group of prticipnts performed the sme tsk while being scnned using fmri (Fig. 2d,e). Moreover, in both experiments, men pupil diltion response did not correlte with overll tsk performnce (behviorl experiment: n = 35, r =.13, P =.44; imging experiment: n = 3, r =.4, P =.82), men rection times (following log trnsform; behviorl experiment: n = 35, r =.17, P =.33; imging experiment: n = 3 prticipnts, r =.6, P =.77), or with nswers to debriefing questions regrding interest, motivtion nd ttention (Supplementry Tble 1). These results suggest tht the reltionship between pupil responses nd dherence to one s lerning predisposition cnnot be explined in terms of fluctutions in overll level of rousl or ttention to the tsk. Further nlysis confirmed tht the decrese in correltion between ILS score nd tsk performnce for prticipnts with higher pupil response (lower gin) ws not simply result of more limited rnge of ILS scores for these prticipnts (Supplementry Fig. 2). Tsk performnce c d Tsk performnce Semntic Visul Pupil dimeter Semntic Visul.4 b r =.28 P = Sensing Intuitive ILS score 11% 15% 1% 95% Tril onset r =.37 P < Sensing Intuitive Men pupil response (%) ILS score e Correltion between ILS score nd tsk performce Time (s) Correltion between ILS score nd tsk performnce r =.96 P < Men pupil response (%) Bseline dimeter r =.93 P <.5 Pupil dimeter nd fmri indictors of gin We next exmined the fmri dt for evidence of fluctutions in gin in individul prticipnts nd tested whether these correlte with pupillry indices of gin. Incresed gin entils tht neurl ctivtion is driven towrd mximl or miniml levels (Fig. 1). Thus, lrge bseline pupil dimeter should be ssocited with more extreme fmri ctivtions. Indeed, we found tht the fmri blood oxygen Men response strength (regression coefficient) Bseline pupil dimeter ** ** NS.5.5 Pupil response Pupil diltion response ** * NS 4 15 Tsk-relevnt stimuli Tsk-irrelevnt stimuli Figure 3 Reltionship between pupil dimeter nd BOLD response to tsk-relevnt nd tsk-irrelevnt stimuli. High bseline pupil dimeter ws ssocited with weker response to tsk-relevnt stimuli compred with tsk-irrelevnt stimuli, wheres high pupil diltion response ws ssocited with stronger response to tsk-relevnt stimuli compred with tsk-irrelevnt stimuli (n = 28 prticipnts). N.S. indictes not significnt (P =.48 for bseline dimeter, P =.99 for diltion response), *P <.5, **P < 1 4. Errors brs indicte between-subjects s.e.m. nture NEUROSCIENCE dvnce online publiction

4 213 Nture Americ, Inc. All rights reserved. Figure 4 Simultion of the effect of globl chnges in gin on functionl connectivity strength nd clustering. Recurrent neurl networks were composed of 1, fully connected units with rndom connection weights. Unit-to-unit correltions were computed cross 5 trils for ech level of gin for ech of 1 networks. () Distribution of correltion coefficients for ech of 15 different levels of gin. Higher gin resulted in stronger functionl connections (correltions or nti-correltions). (b) Men correltion coefficient increses s function of gin (s.e.m. ws too smll to observe). Different simultions in which ech unit ws only connected to minority of other units (1%) or in which correltions were mesured between the men ctivity of groups of ten units yielded qulittively similr results. This suggests tht our results re robust to network density nd mesurement grnulrity. (c) Correltion between the globl gin prmeter nd the frequency of correltion coefficients s function of correltion coefficient. Stronger correltions were more prevlent (nd weker correltions were less prevlent) when gin ws higher. (d) Clustering coefficient of the networks functionl connectivity grphs s function of gin. Clustering coefficient tended to increse with gin. s.e.m., in lighter shde, is brely visible. level dependent (BOLD) signl ws frther from the men when bseline pupil dimeter ws lrge (men bsolute devition from the men = 8.36) thn when it ws smll (men bsolute devition = 8.2; t 29 = 3.79, P < 1 4, pired t test compring 1% of trils with highest pupil dimeter to 1% of trils with lowest pupil dimeter). An dditionl prediction tht stems from the ssumed reltionship between pupil dimeter nd gin is tht the mgnitude of pupil diltion in response to tsk-relevnt stimuli should correlte with the mgnitude of the BOLD response to tsk-relevnt stimuli, but not to tsk-irrelevnt stimuli 5. To test this prediction, we included rndom, tsk-irrelevnt, uditory stimuli tht prticipnts were instructed to ignore. As predicted, both low bseline pupil dimeter nd high pupil diltion response were ssocited with stronger BOLD responses to tsk-relevnt stimuli, but not to tsk-irrelevnt stimuli (bseline dimeter: t 27 = 5.4, P < 1 5 ; diltion response: t 27 = 2.56, P <.5; Fig. 3). b 3, Frequency c Correltion between gin nd frequency of correltion Correltion coefficient Correltion coefficient Neurl gin b.2 Pupil dimeter nd globl fluctutions connectivity The neurl network lerning model described bove suggests tht globl chnges in gin should be ssocited with globl chnges in the strength of functionl connections. To exmine this suggestion in more generl setting, we simulted the effects of gin on functionl connectivity (unit-to-unit correltions) in lrge (1, units) rndomly constructed networks tht were not designed to perform ny prticulr tsk (Fig. 4). This simultion lso suggested tht high gin should be ssocited with stronger men functionl connectivity (r =.99, P < 1 13 ; Fig. 4 c). Thus, we first exmined whether our fmri dt ws chrcterized by globl fluctutions in the strength of functionl connections. We mesured functionl connectivity while prticipnts performed the lerning tsk nd ssessed the extent to which fluctutions in functionl connectivity in different brin res were correlted with ech other. To do this, we rbitrrily divided ech prticipnt s brin into 32 boxes tht contined roughly similr volumes of gry mtter (27.1 ± 2.6 cm 3 ; Fig. 5). We then mesured the men strength of functionl connectivity in ech box during ech gme (quntified s the men bsolute correltion of the time series of the fmri signl mong pirs of voxels in the box). Finlly, we correlted the time series of men functionl connectivity vlues over gmes for ech pir of boxes. Men functionl connectivity strength cross gmes ws positively correlted for 96% of ll box pirs nd the men correltion coefficient ws.72 (rnge = cross prticipnts, t 29 = 1.85, P < 1 1 ; Fig. 5b). Notbly, this correltion did not simply reflect common globl signl component, s the men gry-mtter signl ws regressed out of the dt before the functionl connectivity nlysis. Men correltion coefficient d.35 Clustering coefficient Gin Gin Number of box pirs 2, 1 1, Men correltion between boxes men functionl connectivity strength Number of prticipnts Figure 5 Globl fluctutions in locl functionl connectivity. () Threedimensionl rendering of one prticipnt s gry-mtter voxels divided into 32 boxes, viewed from the right nd from bove. Ech sphere represents voxel. Adjcent boxes re denoted in different colors. Voxel division is visulized using custom-mde softwre creted in the Processing progrmming environment 36. (b) Histogrm of between-box correltions of men within-box functionl connectivity strength (light blue, left y xis), nd of prticipnts men correltion vlues (drk blue, right y xis). dvnce online publiction nture NEUROSCIENCE

5 213 Nture Americ, Inc. All rights reserved. Figure 6 Pupil dimeter nd whole-brin functionl connectivity. () Distribution of functionl connections by connection strength (n = 28 prticipnts). The distribution is shown seprtely for ll gmes (gry shding), for the third of ech prticipnt s gmes in which the prticipnt s bseline pupil dimeter ws lowest (solid line) nd for the third of gmes in which pupil dimeter ws highest (dshed line). Insets: mgnifiction of boxed res to show differences between lowest nd highest pupil dimeter gmes. (b) Gme-by-gme correltion between bseline pupil dimeter nd frequency of functionl connectivity mesurements s function of functionl connectivity vlue. The y xis indictes whether lrge pupil dimeter ws ssocited with more (positive vlues) or fewer (negtive vlues) voxel pirs. For ech prticipnt, we computed the distribution of functionl connections during ech gme nd then computed the correltion cross gmes between bseline pupil dimeter nd the number of voxel pirs in ech bin of the distribution. The curve shows the correltions verged over prticipnts (gry indictes s.e.m.). Lrger pupil dimeter ws ssocited with more strong functionl connectivity mesurements (bsolute strength >.17) nd fewer wek functionl connectivity mesurements (between.17 nd +.17). Thus, even though our mesurements of functionl connectivity in different boxes involved strictly disjoint brin res, we found very strong correltions in fluctutions of these mesurements throughout the brin. Although this result is strongly suggestive of globl modultion of neurl signling, it is nevertheless possible tht globl fluctutions in Proportion of boxes within which functionl connectivity strength correlted positively with bseline pupil dimeter b 1% 8% 6% 4% 2% % Prticipnt number c b Correltion between pupil dimeter nd frequency of functionl connections Proportion of boxes within which functionl connectivity strength correlted negtively with pupil diltion response d Frequency 1% 8% 6% 4% 2% Connection strength All gmes Lowest pupil dimeter Highest pupil dimeter Connection strength locl functionl connectivity reflect correlted locl instbilities of the MRI scnner. Such confound could be dismissed if the mesured fluctutions in functionl connectivity were lso to covry with the seprtely ttined mesures of bseline pupil dimeter. Indeed, we found tht when bseline pupil dimeter ws highest (indictive of high gin), high functionl connectivity mesurements were more prevlent, wheres weker functionl connectivity ws more prevlent when bseline pupil dimeter ws lowest (low gin) (Fig. 6). Accordingly, bseline pupil dimeter ws positively correlted with men functionl connectivity strength (men r =.27 cross prticipnts, t 27 = 2.98, P <.1), nd, similrly, pupil responses were nticorrelted with men functionl connectivity strength (men r =.24 cross prticipnts, t 27 = 3.63, P <.1). In prticulr, bseline dimeter ws positively correlted with the number of functionl connectivity mesurements stronger thn ±.17 nd nticorrelted with the number of weker functionl connectivity mesurements (Fig. 6b). Notbly, this nonmonotonic reltionship between functionl connectivity strength nd its correltions with bseline dimeter ws predicted by % Prticipnt number Men correltion between box functionl connectivity strength nd bseline pupil dimeter Prticipnt number Men correltion between box functionl connectivity strength nd pupil diltion response Prticipnt number Figure 7 Pupil dimeter nd locl functionl connectivity. (,c) Proportion of boxes in which men functionl connectivity strength ws positively correlted with bseline pupil dimeter () or negtively correlted with pupil diltion response (c) for ech prticipnt. (b,d) Men correltion between within-box functionl connectivity strength nd bseline pupil dimeter (b) or pupil diltion response (d) for ech prticipnt. The solid horizontl lines indicte the group mens nd the dshed horizontl lines indicte s.e.m. nture NEUROSCIENCE dvnce online publiction

6 213 Nture Americ, Inc. All rights reserved. our simultion of the effects of gin on functionl connectivity in rndomly connected neurl networks (Fig. 4b). To verify tht the reltionship between functionl connectivity nd pupil dimeter ws not specific to prticulr brin regions, but rther ws mnifest throughout the brin, we exmined this reltionship seprtely in ech of the 32 boxes. Functionl connectivity strength ws positively correlted with bseline pupil dimeter in 7% of the boxes (22 of 32 boxes per prticipnt on verge; Fig. 7) nd the men correltion coefficient ws.19 (t 27 = 3.19, P <.1; Fig. 7b). Notbly, bseline pupil dimeter ws not correlted with the men box fmri signl (men r =.4, t 27 =.35, P =.73), indicting tht the reltionship with functionl connectivity strength did not reflect pupil-relted vritions in signl strength. Furthermore, the reltionship between bseline dimeter nd functionl connectivity strength ws firly consistent throughout the brin: for every box, functionl connectivity ws positively correlted with bseline pupil dimeter in t lest hlf of the prticipnts. As expected, functionl connectivity strength ws lso nticorrelted with pupil diltion response in 75% of the boxes (Fig. 7c) nd the men correltion coefficient ws.2 (t 27 = 3.54, P <.1; Fig. 7d). Thus, our results suggest tht the strength of functionl connectivity fluctutes in similr mnner throughout the brin nd tht these fluctutions re trcked closely by both pupil dimeter indices. Pupil dimeter, neurl clustering nd tsk performnce The results of our lerning neurl network model lso suggest tht, with high gin, functionl connectivity should be tightly clustered rther thn evenly distributed. To exmine this in more generl setting, we constructed functionl connectivity grph for ech of the rndom 1,-unit networks described bove. Ech of the grphs nodes represented unit, nd two units were connected by n edge if the correltion of ctivity between them ws in the top 1% of ll such correltions. The clustering coefficient 18 of such grph indictes the degree to which functionl connectivity is tightly clustered in the network. As expected, higher gin ws ssocited with higher clustering coefficients (r =.99, P < 1 12 ; Fig. 4d). To test the degree to which functionl connectivity ws tightly clustered in the brin, we then constructed functionl connectivity grph for ech prticipnt nd ech gme (18 grphs per prticipnt). The grphs were constructed in the sme mnner s those for the simulted networks, except tht, in this cse, ech of the grphs nodes represented voxel (Supplementry Figs. 3 nd 4). As predicted, we found significnt gme-by-gme correltion between the clustering coefficient of these grphs nd bseline pupil dimeter (men r =.14 cross prticipnts, t 27 = 1.82, P <.5, one tiled; Fig. 8). Tht is, when prticipnts pupil dimeter indicted high gin, their neurl functionl connectivity tended to be more tightly clustered. Moreover, we found similr correltion when the nlysis ws restricted to prefrontl cortex, n re tht is not involved in primry visul processing (men r =.14 cross prticipnts, t 27 = 2.5, P <.5), suggesting tht the reltionship between pupil dimeter nd clustering ws indeed result of globl fluctutions in gin nd not of differences in ctivtion to the visul stimuli. Finlly, to the extent tht functionl connectivity clustering reflects the effects of gin on ttention, we expected the degree of clustering to be ssocited with the degree to which lerning ws focused on stimulus fetures to which the individul ws predisposed to ttend. Consistent with this prediction, we found significnt gme-by-gme correltion between the clustering coefficient nd shift in lerning performnce towrd the type of feture tht the ILS scores indicted s preferred by ech prticipnt (men r =.8 cross prticipnts, Correltion between clustering coefficient nd bseline pupil dimeter Prticipnt number b Correltion between clustering coefficient nd tsk performnce t 29 = 2.2, P <.5). Concordntly, ILS score ws correlted with the reltionship between clustering coefficient nd tsk performnce (r =.35, P <.5; Fig. 8b). Thus, when prticipnts neurl functionl connections were more tightly clustered, tsk performnce more strongly reflected individul predispositions. DISCUSSION We investigted the reltionship between globl, brin-wide fluctutions in neurl gin nd the effect of individul priors or ttentionl predispositions (so-clled lerning styles) on tril-nd-error lerning behvior. More specificlly, we used pupil-dimeter mesures s proxy for globl levels of neurl gin to test the hypothesis tht predispositions constrin lerning more strongly when gin is higher. In two experiments, the degree to which lerning performnce followed individul predisposition ws strongly correlted with pupil response. In support of our interprettion of this correltion, we found tht brin function ws chrcterized by globl fluctutions in the strength of functionl connectivity, s would be expected from globl modultion of gin, nd tht these fluctutions were trcked by pupillry indices of gin. We lso found tht these pupillry indices were correlted with the degree to which functionl connectivity is clustered, s ws predicted by our neurl network modeling. Finlly, we showed tht increses in such clustering were ssocited with shift in the content of lerning towrd the type of informtion tht individul prticipnts were predisposed to process. Tken together, these results provide strong converging evidence in fvor of the hypothesis tht high gin constrins the type of informtion tht is lerned from multidimensionl sensory input in ccordnce with one s prior processing dispositions. The finding tht locl fluctutions in functionl connectivity re globlly correlted cross the brin, nd tht these fluctutions re correlted with pupillry indices, supports existing theory tht implictes the LC-NE system in globl modultion of neurl gin 4,5. We note, however, tht the reltionship between gin nd functionl connectivity my not be s simple s portryed here, s result of fctors such s sturtion of neurl ctivity, network dynmics nd spiking dynmics. Nevertheless, our results, viewed in the context of existing evidence nd theory, conform to the expecttion tht gin nd functionl connectivity should covry. In ddition, our clustering nlysis findings extend this theory by suggesting tht high gin is ssocited with shift from widely distributed pttern of neurl processing to more tightly clustered pttern dominted by the strongest input strems. Semntic Visul r =.35 P < Sensing Intuitive ILS score Figure 8 The clustering of functionl connections, pupil dimeter nd tsk performnce. () Gme-by-gme correltion between clustering coefficient nd bseline pupil dimeter by prticipnt. (b) Gme-by-gme correltion between clustering coefficient nd visul-semntic performnce difference in tsk s function of sensing-intuitive score on the ILS questionnire (n = 3 prticipnts). dvnce online publiction nture NEUROSCIENCE

7 213 Nture Americ, Inc. All rights reserved. Our results provide neurl-computtionl frmework in which pst findings concerning the reltionship of stress nd norepinephrine levels to cognitive function cn be understood. A lrge body of psychologicl reserch in humns suggests tht stress (which is ssocited with high levels of norepinephrine) reduces the bredth of ttention 19,2. Another set of studies found tht stress nd norepinephrine shift rt nd humn behvior from flexible mode of behvior to more rigid hbitul mode in which previously estblished stimulus-response ssocitions re followed Stress nd norepinephrine hve lso been linked to diminished performnce in tsks requiring cognitive flexibility 25,26. Our findings suggest n explntion of these previously observed phenomen in terms of the influence of the LC-NE system in globlly modulting neurl gin. Incresed gin nrrows ttention by strengthening lredy strong neurl representtions t the expense of competing weker representtions. This, in turn, fvors previously estblished ptterns of behvior, which re subserved by well-estblished neurl circuits nd tend to form stronger representtions. We ttempted to identify the effects of neurl gin, computtionl concept defined in terms of the input-output function of neurl units, on behvior nd on whole-brin fmri metrics. This constitutes promising pproch by which low-level principles of neurl function my be linked vi computtionl modeling to system-level neurl nd behviorl phenomen. However, the disdvntge of our pproch is tht it necessrily relies on brod set of ssumptions. Specificlly, in mking our predictions, we ssumed tht chnges in pupil dimeter would trck chnges in neurl gin. Furthermore, our fmri predictions were bsed on the ssumption tht the BOLD signl would reflect the neurl effects of gin simulted by chnges in firing rtes in our computtionl models. This lst ssumption is prticulrly tenuous, s severl studies hve found dissocitions between spiking ctivity nd the BOLD signl specificlly under conditions tht re thought to involve chnges in neuromodultion Nevertheless, we present diverse set of behviorl nd imging results tht precisely mtch the predictions mde by our neurl network simultions of the effect of gin on neurl ctivity, connectivity nd behvior. This set of converging results, in ddition to evidence from pst studies, provides substntil support for our underlying ssumptions. The focusing effect of neurl gin on processing my t first glnce seem to conflict with previous ccounts suggesting tht toniclly high gin reduces tsk-focused ttention 5. However, lthough our findings suggest tht incresed gin focuses ttention on predisposed dimensions of sensory stimuli, these need not be relted to the tsk t hnd. Rther, if distrcting stimuli re slient enough to evoke strong neurl representtions, our theory predicts tht high gin would be ssocited with incresed ttention to distrcters nd with reduced tsk-focused ttention. Our findings lso fit well with previous suggestion 3 tht phsic norepinephrine responses, which re stronger in low gin sttes (low tonic LC-NE ctivity), fcilitte behviorl flexibility in response to unexpected trget stimuli. Severl of our results drw on grph-theoretic methods tht hve incresingly been used to nlyze both structurl nd functionl brin imging dt 31,32. The strength of these methods lies in their bility to cpture, by simple quntittive mesures, chrcteristics of networks tht re comprised of very lrge number of elements. Most previous studies employing grph-theoretic nlyses hve investigted sttionry spects of neurl processing networks, but few recent studies hve begun to exmine how mesures of functionl brin network topology vry with behvior The ltter, however, nlyzed reltively smll networks (<12 nodes). In contrst, we used grphtheoretic mesures to exmine how the topology of high-resolution whole-brin networks (>2, nodes) vries with behvior. Our results indicte tht such n nlysis cn provide meningful insights into the wy sensory informtion is processed nd lerned. In conclusion, our findings suggest tht processing predispositions cn influence lerning, but tht these priors re not lwys binding. Rther, brin-wide fluctutions in neurl gin induce different modes of neurl communiction tht modulte the bredth of ttention nd the extent to which processing nd lerning re constrined by prior dispositions. The dptive vlue of modulting this spect of processing in ccord with situtionl vribles is cler. The questions now re wht drives these chnges in gin nd how does the brin determine wht mode of processing is suitble t ny given moment. Methods Methods nd ny ssocited references re vilble in the online version of the pper. Note: Supplementry informtion is vilble in the online version of the pper. Acknowledgments We thnk N. Turk-Browne nd P. Dyn for helpful comments on erlier versions of the mnuscript. This reserch ws funded by US Ntionl Institutes of Helth grnts R3 DA2973 nd R1 MH98861, Howrd Hughes Medicl Institute Interntionl Student Reserch fellowship to E.E. nd Slon Reserch Fellowship to Y.N. The uthors lso wish to thnk the generous support of the Regin nd John Scully Center for the Neuroscience of Mind nd Behvior in the Princeton Neuroscience Institute. AUTHOR CONTRIBUTIONS E.E. nd Y.N. designed the study with consulttion from J.D.C. E.E. nd Y.N. nlyzed the dt, nd ll of the uthors contributed to discussion nd interprettion of the findings nd writing the mnuscript. COMPETING FINANCIAL INTERESTS The uthors declre no competing finncil interests. Reprints nd permissions informtion is vilble online t reprints/index.html. 1. Felder, R.M. & Silvermn, L.K. Lerning nd teching styles in engineering eduction. Eng. Educ. 78, (1988). 2. Coffield, F., Moseley, D., Hll, E. & Ecclestone, K. Lerning Styles nd Pedgogy in Post-16 Lerning: Systemtic nd Criticl Review (Lerning nd Skills Reserch Centre, London, 24). 3. Felder, R.M. & Spurlin, J. Appliction, relibility nd vlidity of the index of lerning styles. Int. J. Eng. Educ. 21, (25). 4. Servn-Schreiber, D., Printz, H. & Cohen, J.D. A network model of ctecholmine effects: gin, signl-to-noise rtio, nd behvior. Science 249, (199). 5. Aston-Jones, G. & Cohen, J.D. An integrtive theory of locus coeruleus norepinephrine function: dptive gin nd optiml performnce. Annu. Rev. Neurosci. 28, (25). 6. Gilzenrt, M.S., Nieuwenhuis, S., Jepm, M. & Cohen, J.D. Pupil dimeter trcks chnges in control stte predicted by the dptive gin theory of locus coeruleus function. Cogn. Affect. Behv. Neurosci. 1, (21). 7. Jepm, M. & Nieuwenhuis, S. Pupil dimeter predicts chnges in the explortionexploittion trde-off: evidence for the dptive gin theory. J. Cogn. Neurosci. 23, (211). 8. Wterhouse, B.D., Moises, H.C. & Woodwrd, D.J. Nordrenergic modultion of somtosensory corticl neuronl responses to lontophoreticlly pplied puttive neurotrnsmitters. Exp. Neurol. 69, 3 49 (198). 9. Wterhouse, B.D., Moises, H.C., Yeh, H.H., Geller, H.M. & Woodwrd, D.J. Comprison of norepinephrine- nd benzodizepine-induced ugmenttion of Purkinje cell responses to gmm-minobutyric cid (GABA). J. Phrmcol. Exp. Ther. 228, (1984). 1. Wterhouse, B.D. & Woodwrd, D.J. Interction of norepinephrine with cerebrocorticl ctivity evoked by stimultion of somtosensory fferent pthwys in the rt. Exp. Neurol. 67, (198). 11. Koss, M.C. Pupillry diltion s n index of centrl nervous system α 2 -drenoceptor ctivtion. J. Phrmcol. Methods 15, 1 19 (1986). 12. Einhäuser, W., Stout, J., Koch, C. & Crter, O.L. Pupil diltion reflects perceptul selection nd predicts subsequent stbility in perceptul rivlry. Proc. Ntl. Acd. Sci. USA 15, (28). 13. Murphy, P.R., Robertson, I.H., Blsters, J.H. & O Connell, R.G. Pupillometry nd P3 index the locus coeruleus-nordrenergic rousl function in humns. Psychophysiology 48, (211). nture NEUROSCIENCE dvnce online publiction

8 213 Nture Americ, Inc. All rights reserved. 14. Slins, E. Fst rempping of sensory stimuli onto motor ctions on the bsis of contextul modultion. J. Neurosci. 24, (24). 15. Slins, E. & Bentley, N.M. Gin modultion s mechnism for switching reference frmes, tsks nd trgets. Coherent Behv. Neuronl Netw. 3, (29). 16. Hider, B. & McCormick, D.A. Rpid neocorticl dynmics: cellulr nd network mechnisms. Neuron 62, (29). 17. Eguíluz, V.M., Chilvo, D.R., Cecchi, G.A., Bliki, M. & Apkrin, A.V. Scle-free brin functionl networks. Phys. Rev. Lett. 94, 1812 (25). 18. Luce, R.D. & Perry, A. A method of mtrix nlysis of group structure. Psychometrik 14, (1949). 19. Esterbrook, J.A. The effect of emotion on cue utiliztion nd the orgniztion of behvior. Psychol. Rev. 66, (1959). 2. Stl, M.A. Stress, Cognition, nd Humn Performnce: Literture Review nd Conceptul Frmework (NASA STI Progrm, 24). 21. Dis-Ferreir, E. et l. Chronic stress cuses frontostritl reorgniztion nd ffects decision-mking. Science 325, (29). 22. Schwbe, L. & Wolf, O.T. Stress-induced modultion of instrumentl behvior: from gol-directed to hbitul control of ction. Behv. Brin Res. 219, (211). 23. Schwbe, L., Tegenthoff, M., Höffken, O. & Wolf, O.T. Concurrent glucocorticoid nd nordrenergic ctivity shifts instrumentl behvior from gol-directed to hbitul control. J. Neurosci. 3, (21). 24. Schwbe, L., Höffken, O., Tegenthoff, M. & Wolf, O.T. Preventing the stress-induced shift from gol-directed to hbit ction with β-drenergic ntgonist. J. Neurosci. 31, (211). 25. Alexnder, J.K., Hillier, A., Smith, R., Tivrus, M. & Beversdorf, D. Bet-drenergic modultion of cognitive flexibility during stress. J. Cogn. Neurosci. 19, (27). 26. Cmpbell, H.L., Tivrus, M.E., Hillier, A. & Beversdorf, D.Q. Incresed tsk difficulty results in greter impct of nordrenergic modultion of cognitive flexibility. Phrmcol. Biochem. Behv. 88, (28). 27. Mier, A. et l. Divergence of fmri nd neurl signls in V1 during perceptul suppression in the wke monkey. Nt. Neurosci. 11, (28). 28. Sirotin, Y.B. & Ds, A. Anticiptory hemodynmic signls in sensory cortex not predicted by locl neuronl ctivity. Nture 457, (29). 29. Logothetis, N.K. Wht we cn do nd wht we cnnot do with fmri. Nture 453, (28). 3. Dyn, P. & Yu, A.J. Norepinephrine nd neurl interrupts. in Advnces in Neurl Informtion Processing Systems 18 (eds. Weiss, Y., Schölkopf, B. & Pltt, J.) (MIT Press, Cmbridge, Msschusetts, 26). 31. Bullmore, E. & Sporns, O. Complex brin networks: grph theoreticl nlysis of structurl nd functionl systems. Nt. Rev. Neurosci. 1, (29). 32. Bullmore, E. & Sporns, O. The economy of brin network orgniztion. Nt. Rev. Neurosci. 13, (212). 33. Bssett, D.S. et l. Dynmic reconfigurtion of humn brin networks during lerning. Proc. Ntl. Acd. Sci. USA 18, (211). 34. Kitzbichler, M.G., Henson, R.N., Smith, M.L., Nthn, P.J. & Bullmore, E.T. Cognitive effort drives workspce configurtion of humn brin functionl networks. J. Neurosci. 31, (211). 35. Nicol, R.M. et l. Fst reconfigurtion of high-frequency brin networks in response to surprising chnges in uditory input. J. Neurophysiol. 17, (212). 36. Res, C. & Fry, B. Processing: Progrmming Hndbook for Visul Designers nd Artists (MIT Press, Cmbridge, Msschusetts, 27). dvnce online publiction nture NEUROSCIENCE

9 213 Nture Americ, Inc. All rights reserved. ONLINE METHODS Lerning neurl network model. We modeled lerning of stimulus-rewrd mpping from exmples using three-lyer neurl network. The network consisted of four-node stimulus input lyer, in which the stimulus ws represented using two types of fetures (for exmple, in the cse of our tsk, semntic nd visul fetures of the stimulus), representtion middle lyer nd rewrd output lyer in which ctivity represented the expected rewrd (Fig. 1c,d). As in our tsk, there were two possible fetures in ech type, one of which ws ssocited with rewrd output. Our im ws to exmine how ssocitive lerning chnges s function of gin nd of the network s predisposition to represent either of the stimulus fetures more strongly. Thus, ech stimulus consisted of binry input vector in which one input feture of ech type ws set to 1 (nd the rest were set to ) nd the weights ssocited with ech input reflected the degree to which the network ws predisposed to represent tht type of feture. In ddition, middle lyer units inhibited ech other (weight = 1) to simulte competition for ttention between different representtions. Unit i ctivtion ws computed s i = f wij j j where w ij is the connection weight from unit j to unit i, nd f(x) is the sigmoid ctivtion function 1 f ( x) = + gin x 1 e The prmeter gin reflected the level of neurl gin in the network nd hd the sme vlue for ll units. To determine how much inhibition ech middle lyer unit should exert, the ctivtion level of ech unit ws first computed on the bsis of the input from the input lyer. The resulting vlues were then used to compute the mgnitude of lterl inhibition in the middle lyer, nd ctivtion levels were recomputed. We rn the simultion with 15 different vlues of gin between.1 nd 2, nd with 16 different settings of predisposition to one of the input strems for ech level of gin. Network predisposition to represent feture 1 reltive to feture 2 ws vried between 1/2 nd 2/1, nd input-to-middle lyer weights were set ccordingly, under the constrint tht both weights sum to 1 (for exmple, for rtio of 1/2, weight 1 ws set s.33 nd weight 2 ws set s.67). On ech run, ech of the four possible stimuli nd its ssocited rewrd output were presented to the network. The network s tsk ws to lern to ssocite between the rewrd-ssocited fetures nd rewrd output, nd we exmined the extent to which tht ws lerned for ech of the input strems. Lerning proceeded s follows: weights from the middle lyer units to the output unit (w oi ) were initilized to nd the output unit ctivtion (o) ws computed ccording to eqution (1). Then, the difference between the trget (t) nd ctul output ctivtion ws used to updte the weights to the output unit using the delt rule 37 woi = woi + ( t o) f wojj i j where f (x) is the derivtive of the ctivtion function, which in this cse is f ( x) = gin f ( x)( 1 f ( x)) (1) (2) (3) (4) The lerned weights w oi reflected the degree to which the network lerned to ssocite ech of the stimulus fetures with the rewrd output. We therefore used the rtio between these weights to represent the bis in lerning performnce towrd either of the rewrd-ssocited fetures. It is esy to see tht the only term tht differentites between the updte equtions of the two weights is i, the ctivtion of the respective middle lyer unit. Indeed, the rtio between the lerned weights followed the rtio between the ctivtion of the two middle lyer units. Ech run ws repeted 1 times, with rndom ordering of the stimuli, nd the resulting weight rtios were verged. Rndomly constructed neurl network model. To exmine the effects of gin on functionl connectivity in generl setting, we constructed recurrent neurl network of 1, fully connected units. Weights were rndomly smpled from uniform distribution between.1 nd.1. On every tril, ctivtions were rndomly smpled from uniform distribution between nd 1, nd then updted in rndom order using eqution (1) until ech unit ws updted five times. The sme gin ws used for ll units. The end stte ws considered s the ctivtion pttern of tht tril. For ech level of gin, we conducted 5 trils nd computed the degree to which ech pir of units ws correlted cross trils. The full unit-to-unit correltion mtrix ws used to compute the clustering coefficient s described below for the fmri dt. We repeted the simultion 1 times, ech time with different rndomly determined weight mtrix. The gin prmeter ws limited to vlues which did not result in consistent widespred sturtion (tht is, so tht on verge most units re neither bove 95% nor below 5% of the mximl ctivtion). We tested the reltionship between gin nd men unit-to-unit correltion in two dditionl lterntive settings: when ech unit is only connected to minority (1%) of the other units, nd when correltions re mesured between groups of ten units insted of between single units. Results were qulittively similr nd re therefore not shown. Prticipnts. 36 nive prticipnts (men ge = 25.1 yers, ge rnge = yers, 22 femles) performed the behviorl experiment nd 35 nive prticipnts (men ge = 2.5 yers, ge rnge = 18 3 yers, 25 femles) performed the fmri experiment. Prticipnts were from the Princeton University re, nd gve written informed consent before tking prt in the study, which ws pproved by Princeton University s institutionl review bord. Prticipnts in the behviorl experiment received monetry compenstion ccording to their performnce on the tsk ($.6 per rewrd point, $ totl, men $14.88). fmri prticipnts received monetry compenstion for their time, s well s bonus ccording to their performnce ($.4 per rewrd point, $ , men = $9.47). Stimuli. The experiment involved 18 stimulus sets, hlf of which consisted of imges of objects nd the other hlf consisted of imges of words. Words were generted using the Processing progrmming environment 36, nd object imges were collected from vrious sources on the internet using the Cretive Commons serch interfce ( nd edited using Adobe Photoshop CS5 (Adobe Systems). To minimize luminnce-relted chnges in pupil dimeter, ll stimuli were mde isoluminnt with the bckground, to best pproximtion. Word colors were djusted to be isoluminnt using the flickerfusion procedure 38 on the disply systems tht were used in ech experiment. More complex imges, which consisted of mny colors, were djusted by scling ll colors so s to equte the men estimted luminnce with the bckground. For this purpose, luminnce of ech color ws estimted bsed on its RGB vlues s.2126 R G B ( The men devition of luminnce in imges ws 29% (rnge % to 76%). Given tht within-imge vrince nd devition of the disply system from the srgb stndrd might cuse slight differences in luminnce perception, ll of the nlyses bsed on pupil diltion response were repeted using pupil responses to word stimuli only, which did not suffer from these sources of vrince. The results of these nlyses were similr to those reported here nd re therefore not reported for the ske of brevity. Stimuli were presented using MATLAB softwre (MthWorks) nd the Psychophysics Toolbox 39 on computer monitor (behviorl experiment) or using projector outside the MRI scnner tht displyed the stimuli onto trnslucent screen locted t the end of the scnner bore (fmri experiment), which prticipnts viewed through mirror ttched to the hed coil. To compre BOLD responses to tsk-relevnt nd tsk-irrelevnt stimuli, we plyed 72 tsk-irrelevnt uditory stimuli (phonemes), which prticipnts were instructed to ignore, t rndom times during the inter-tril intervls in the fmri experiment (four stimuli per gme). The phonemes were obtined from wikipedi.org/ nd were, t most, 1 s long. Behviorl tsk. Prticipnts chose between pirs of stimuli nd received monetry rewrd ccording to their choices. On ech tril, prticipnts hd 3 s to choose between two stimuli, fter which the rewrd ws presented for 2 s. Inter-tril intervl ws vried rndomly (uniformly) between 6 nd 1 s. We used reltively long inter-tril intervl to llow enough time following ech tril for doi:1.138/nn.3428 nture NEUROSCIENCE